Applied Computational Psychiatry
Research team
Our vision is to develop computational tools with real potential for clinical applications.
When an organ is unable to meet the demands placed on it, illness can arise. As the main functions of the brain are to compute and learn, an understanding of mental illnesses will benefit from an understanding of the computational and learning functions the brain performs, and how these are affected in states of ill-health.
The Applied Computational Psychiatry group focuses on developing computational tools with real potential for clinical applications. While we are fascinated by the brain and by computational methods in general, our research invests most in those aspects which we think are most likely to result in treatments.
Current projects:
- The use of computational modelling, neuroimaging and behaviour – to understand what happens when patients stop taking antidepressant and how this leads to relapse. We then attempt to use this knowledge to predict who will relapse and hence aid clinical decision-making.
- The use of computational modelling, neuroimaging and behaviour to understand how decision-making and learning contribute to the development, maintenance and relapse in alcohol dependence.
- Development of MEG-decoding approaches to understand automatic negative thoughts.
- Development of computational methods to understand affect dynamics.
Max Planck UCL Centre for Computational Psychiatry and Ageing ResearchÂ
Team


- Special Issue on Reliable Mechanisms for Translational Applications Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 8 (8), 778-779 DOI: 10.1016/j.bpsc.2023.06.004
- Alcohol Approach Bias Is Associated With Both Behavioral and Neural Pavlovian-to-Instrumental Transfer Effects in Alcohol-Dependent Patients Biological Psychiatry Global Open Science, 3 (3), 443-450 DOI: 10.1016/j.bpsgos.2022.03.014
- View all publications by the Applied Computational Psychiatry team